Defuzzification is an important operation in the theory of fuzzy sets. It transforms a fuzzy set information into a numeric data information. This operation along with the operation of fuzzification is critical to the design of fuzzy systems as both of these operations provide nexus between the fuzzy set domain and the real-valued scalar domain. We need the synergy of both of these domains to solve many of our ill-posed problems effectively. In this paper, we address the problem of defuzzification, we present merits and demerits of various defuzzification strategies that are used in the theory and practice, and in design and implementation of applications involving fuzzy theory, fuzzy control, and fuzzy rule base, and fuzzy inference-based systems. We also present in this paper a simple and yet a novel defuzzification mechanism. ᮊ

No abstract

No abstract

No abstract

Fault localization is the process of locating faulty lines of code in a buggy program. This paper presents a novel approach to automate fault localization by combining feature selection (a fundamental concept in machine learning) with mutual information (a fundamental concept in information theory). Specifically, we present a family of generalized entropies for computing generalized mutual information, which enables feature selection. The family generalizes well-known entropies, such as Shannon and Renyi entropies, and lays the foundation of a uniform entropy-based technique for fault localization. We perform an experimental evaluation of our approach using the Siemens suite of subject programs. Experimental results show that while using mutual information based on generalized entropies allows more accurate fault localization that traditional techniques, the specific entropies used do not have a significant impact on fault localization effectiveness.